binsreg-package: Binsreg Package Document

Description Author(s) References


Binscatter provides a flexible, yet parsimonious way of visualizing and summarizing large data sets in regression settings, and has been a popular methodology in applied microeconomics and other social sciences. The binsreg package provides tools for statistical analysis using the binscatter methods developed in Cattaneo, Crump, Farrell and Feng (2019a). binsreg implements binscatter estimation with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band. binsregtest implements hypothesis testing procedures for parametric specification of and nonparametric shape restrictions on the unknown regression function. binsregselect implements data-driven number of bins selectors for binscatter implementation using either quantile-spaced or evenly-spaced binning/partitioning. All the commands allow for covariate adjustment, smoothness restrictions, and clustering, among other features.

The companion software article, Cattaneo, Crump, Farrell and Feng (2019b), provides further implementation details and empirical illustration. For related Stata and R packages useful for nonparametric data analysis and statistical inference, visit


Matias D. Cattaneo, University of Michigan, Ann Arbor, MI.

Richard K. Crump, Federal Reserve Bank of New York, New York, NY.

Max H. Farrell, University of Chicago, Chicago, IL.

Yingjie Feng (maintainer), University of Michigan, Ann Arbor, MI.


Cattaneo, M. D., R. K. Crump, M. H. Farrell, and Y. Feng. 2019a: On Binscatter. Working Paper.

Cattaneo, M. D., R. K. Crump, M. H. Farrell, and Y. Feng. 2019b: Binscatter Regressions. Working Paper.

binsreg documentation built on May 2, 2019, 12:19 p.m.